1. Technical Field
The present invention relates to a method for detecting and measuring local deviations in the shape of planar, curved, or arched surfaces of a test object, with three-dimensional measurements of the surfaces being evaluated with an evaluation device. Further, the present invention relates to a device for detecting and measuring local deviations in the shape of planar, curved, or arched surfaces of a test object, comprising an evaluation device for evaluating three-dimensional measurements.
2. Description of Related Art
Various devices and methods are known in prior art for the detection or measuring of local deviations in the shape of designed and/or functional surfaces. On the one hand, there are manual or contacting methods, and on the other hand optic measuring methods with different evaluation methods.
With regards to the manual methods, a thin polishing of the surface is known via an abrasive block or a flexible pad. Through the thin polishing process, local elevations or recesses as well as concave sections on a surface become visible. They can be detected by the human eye. However, here it is problematic that such methods are only suitable for metallic surfaces and in this case any potential anti-corrosive coating on the surface is damaged. Further, this method requires a trained auditor and additionally it is time-consuming. A manual sensing of a surface via specialty gloves is also known. Here, the auditor detects uneven sections on the surface. It is problematic, though, that it represents a subjective method, which can only be executed by a trained auditor and is also time-consuming. Another method of prior art is the straight edge, the visual inspection of a raw and enameled surface or methods with respective reflections of a projected pattern on a surface. Here, patterns are reflected and potential irregularities of the surface can be rendered visible. In this method the subjectivity as well as the correspondingly long time required are problematic.
Using optic measuring methods allows for possible three-dimensional measuring of surfaces and subsequently the detection and measurement of local deviations from the shape of the surface. For example, the CAD-comparison is known from prior art. Here, the difference between points of 3D-measurements and the CAD-data is calculated and evaluated. Here it is problematic that not only local, but also global deviations are displayed, which regularly exceed the local deviations and mask them.
Further, the method of an associative memory is known from EP 0 921 374 B1. This represents a special, artificial neuronal network. In a calibration process, flawless parts are measured. Using these measurements, the artificial neuronal network is then trained and, upon recalling the network with the measurements of the calibration parts, the errors occurring in the test parts are smoothened. In a subsequent differentiation with the original data, these errors are detected and measured. Here it is disadvantageous that the method can only be used when flawless parts or suitable parts are available.
Further, the method of polynomial approximation is known. Here, measurements are approximated with the help of polynomials or similar methods, by which the data is smoothened. Thereafter the difference between the smoothened data and the original data is calculated, allowing a detection and measurement of flawed sections. Here it is disadvantageous that, particularly in the area of design elements or edges of objects, artifacts can develop, because they are also smoothened. This leads to a poor interpretation of the measuring results by the auditors. Additionally, a mathematical analysis of known flaws can occur by assessing a cross-section through the 3D-data. Here, the difference is shown in reference to an interactively stored line in the data. It is problematic, though, that only known error sections can be analyzed. The evaluation of data cannot be done automatically, but requires trained personnel. A comprehensive assessment of a surface is not possible here.
Finally, the analysis of a curvature is known in prior art. Here, the thin polishing of the surface with an abrasive block can be approximated by a one-dimensional calculation of the curvature based on the 3D-data. However, this method requires the precise knowledge of the extension of the flaw. Further, the result is not equivalent to an image known to the auditor, which he/she obtains after the treatment with an abrasive block.